/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
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package org.apache.spark.examples.ml;

import org.apache.spark.ml.feature.RegexTokenizer;
import org.apache.spark.ml.feature.Tokenizer;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import scala.collection.mutable.Seq;

import java.util.Arrays;
import java.util.List;

import static org.apache.spark.sql.functions.callUDF;
import static org.apache.spark.sql.functions.col;
// $example off$

public class JavaTokenizerExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession
                .builder()
                .appName("JavaTokenizerExample")
                .getOrCreate();

        // $example on$
        List<Row> data = Arrays.asList(
                RowFactory.create(0, "Hi I heard about Spark"),
                RowFactory.create(1, "I wish Java could use case classes"),
                RowFactory.create(2, "Logistic,regression,models,are,neat")
        );

        StructType schema = new StructType(new StructField[]{
                new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
                new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
        });

        Dataset<Row> sentenceDataFrame = spark.createDataFrame(data, schema);

        Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");

        RegexTokenizer regexTokenizer = new RegexTokenizer()
                .setInputCol("sentence")
                .setOutputCol("words")
                .setPattern("\\W");  // alternatively .setPattern("\\w+").setGaps(false);

        spark.udf().register(
                "countTokens", (Seq<?> words) -> words.size(), DataTypes.IntegerType);

        Dataset<Row> tokenized = tokenizer.transform(sentenceDataFrame);
        tokenized.select("sentence", "words")
                .withColumn("tokens", callUDF("countTokens", col("words")))
                .show(false);

        Dataset<Row> regexTokenized = regexTokenizer.transform(sentenceDataFrame);
        regexTokenized.select("sentence", "words")
                .withColumn("tokens", callUDF("countTokens", col("words")))
                .show(false);
        // $example off$

        spark.stop();
    }
}
